{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "210802d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入相关库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pyecharts.charts import Bar\n",
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Scatter\n",
    "from pyecharts.charts import Pie\n",
    "from pyecharts.charts import Radar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9d1b4310",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('./data_house.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ed3825b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>name</th>\n",
       "      <th>way</th>\n",
       "      <th>layout</th>\n",
       "      <th>size</th>\n",
       "      <th>zone</th>\n",
       "      <th>position</th>\n",
       "      <th>location</th>\n",
       "      <th>money</th>\n",
       "      <th>subway</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>可月付 ,天通苑地铁站附近,朝南主卧独卫,配置齐全</td>\n",
       "      <td>天通苑北一区</td>\n",
       "      <td>合租主卧</td>\n",
       "      <td>2户合租</td>\n",
       "      <td>15㎡</td>\n",
       "      <td>昌平</td>\n",
       "      <td>天通苑</td>\n",
       "      <td>朝南</td>\n",
       "      <td>2500</td>\n",
       "      <td>天通苑站</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>亚运村·安苑北里·中楼层·4居室·次卧2</td>\n",
       "      <td>安苑北里</td>\n",
       "      <td>合租主卧</td>\n",
       "      <td>2户合租</td>\n",
       "      <td>15㎡</td>\n",
       "      <td>朝阳</td>\n",
       "      <td>惠新西街</td>\n",
       "      <td>朝北</td>\n",
       "      <td>2350</td>\n",
       "      <td>惠新西街北口站</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>可月付 一号线梨园城铁 物美大卖场 梨花园次卧</td>\n",
       "      <td>梨花园二期</td>\n",
       "      <td>合租次卧</td>\n",
       "      <td>3户合租</td>\n",
       "      <td>15㎡</td>\n",
       "      <td>通州</td>\n",
       "      <td>梨园</td>\n",
       "      <td>朝南北</td>\n",
       "      <td>1350</td>\n",
       "      <td>梨园站</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>VR房源嘉浩别墅捡漏大独栋,自带私家泳池,客厅9米挑空,环绕</td>\n",
       "      <td>嘉浩国际商住别墅城</td>\n",
       "      <td>整租</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>460㎡</td>\n",
       "      <td>顺义</td>\n",
       "      <td>中央别墅区</td>\n",
       "      <td>朝南</td>\n",
       "      <td>45000</td>\n",
       "      <td>无</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>梨园地铁 电梯房 主卧独卫 精装修 三家两卫 定期保洁</td>\n",
       "      <td>梨园小镇</td>\n",
       "      <td>合租主卧</td>\n",
       "      <td>3户合租</td>\n",
       "      <td>20㎡</td>\n",
       "      <td>通州</td>\n",
       "      <td>梨园</td>\n",
       "      <td>朝南</td>\n",
       "      <td>2500</td>\n",
       "      <td>梨园站</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            title       name   way layout  size zone position  \\\n",
       "0       可月付 ,天通苑地铁站附近,朝南主卧独卫,配置齐全     天通苑北一区  合租主卧   2户合租   15㎡   昌平      天通苑   \n",
       "1            亚运村·安苑北里·中楼层·4居室·次卧2       安苑北里  合租主卧   2户合租   15㎡   朝阳     惠新西街   \n",
       "2         可月付 一号线梨园城铁 物美大卖场 梨花园次卧      梨花园二期  合租次卧   3户合租   15㎡   通州       梨园   \n",
       "3  VR房源嘉浩别墅捡漏大独栋,自带私家泳池,客厅9米挑空,环绕  嘉浩国际商住别墅城    整租   4室2厅  460㎡   顺义    中央别墅区   \n",
       "4     梨园地铁 电梯房 主卧独卫 精装修 三家两卫 定期保洁       梨园小镇  合租主卧   3户合租   20㎡   通州       梨园   \n",
       "\n",
       "  location  money   subway  \n",
       "0       朝南   2500     天通苑站  \n",
       "1       朝北   2350  惠新西街北口站  \n",
       "2      朝南北   1350      梨园站  \n",
       "3       朝南  45000        无  \n",
       "4       朝南   2500      梨园站  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ba3a68e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2340, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d3982808",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title       object\n",
       "name        object\n",
       "way         object\n",
       "layout      object\n",
       "size        object\n",
       "zone        object\n",
       "position    object\n",
       "location    object\n",
       "money        int64\n",
       "subway      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eedadbfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2340 entries, 0 to 2339\n",
      "Data columns (total 10 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   title     2340 non-null   object\n",
      " 1   name      2340 non-null   object\n",
      " 2   way       2340 non-null   object\n",
      " 3   layout    2340 non-null   object\n",
      " 4   size      2340 non-null   object\n",
      " 5   zone      2340 non-null   object\n",
      " 6   position  2340 non-null   object\n",
      " 7   location  2338 non-null   object\n",
      " 8   money     2340 non-null   int64 \n",
      " 9   subway    2340 non-null   object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 182.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d534ca48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title       0\n",
       "name        0\n",
       "way         0\n",
       "layout      0\n",
       "size        0\n",
       "zone        0\n",
       "position    0\n",
       "location    2\n",
       "money       0\n",
       "subway      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ee46d71",
   "metadata": {},
   "source": [
    "### 将含有缺失值的两条数据删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf5abf50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(subset='location',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e9d6c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title       0\n",
       "name        0\n",
       "way         0\n",
       "layout      0\n",
       "size        0\n",
       "zone        0\n",
       "position    0\n",
       "location    0\n",
       "money       0\n",
       "subway      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b92a17b",
   "metadata": {},
   "source": [
    "### 分析户型的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8759e933",
   "metadata": {},
   "outputs": [],
   "source": [
    "layout_list = df.layout.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "11b1256b",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = (\n",
    "     Bar()\n",
    "     .add_xaxis(layout_list.index.to_list())\n",
    "     .add_yaxis(\"户型\",layout_list.tolist())\n",
    "     .reversal_axis()\n",
    "     .set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n",
    "     .set_global_opts(title_opts=opts.TitleOpts(title=\"户型统计\"))\n",
    "     .render_notebook()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3f46d01a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"f4e3229c914e416698618c0b1a27d450\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_f4e3229c914e416698618c0b1a27d450 = echarts.init(\n",
       "                    document.getElementById('f4e3229c914e416698618c0b1a27d450'), 'white', {renderer: 'canvas'});\n",
       "                var option_f4e3229c914e416698618c0b1a27d450 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
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       "        \"#546570\",\n",
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       "        \"#ef5b9c\",\n",
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       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u6237\\u578b\",\n",
       "            \"legendHoverLink\": true,\n",
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       "            \"showBackground\": false,\n",
       "            \"barMinHeight\": 0,\n",
       "            \"barCategoryGap\": \"20%\",\n",
       "            \"barGap\": \"30%\",\n",
       "            \"large\": false,\n",
       "            \"largeThreshold\": 400,\n",
       "            \"seriesLayoutBy\": \"column\",\n",
       "            \"datasetIndex\": 0,\n",
       "            \"clip\": true,\n",
       "            \"zlevel\": 0,\n",
       "            \"z\": 2,\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"right\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u6237\\u578b\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u6237\\u578b\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
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       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
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       "        \"textStyle\": {\n",
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       "            \"nameLocation\": \"end\",\n",
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       "            \"splitNumber\": 5,\n",
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       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_f4e3229c914e416698618c0b1a27d450.setOption(option_f4e3229c914e416698618c0b1a27d450);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x21ec6714d10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d39f37bb",
   "metadata": {},
   "source": [
    "### 确定观测价格区间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cf31f28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.money.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "406ae510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_num=df.money.describe([.965])[\"96.5%\"]\n",
    "max_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b28dcce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df.money<max_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ea2da2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.money.plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c50e6862",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 户型对房租的影响\n",
    "layout_money = df.groupby('layout').money.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "49815d4d",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "layout\n",
       "5户合租     1690.0\n",
       "2户合租     2200.0\n",
       "3户合租     2300.0\n",
       "4户合租     2960.0\n",
       "3室0厅     3200.0\n",
       "1室0厅     4600.0\n",
       "1室1厅     5800.0\n",
       "2室1厅     6000.0\n",
       "3室1厅     6600.0\n",
       "2室2厅    11500.0\n",
       "2室4厅    12100.0\n",
       "1室2厅    13000.0\n",
       "3室2厅    18000.0\n",
       "4室1厅    22000.0\n",
       "4室2厅    32000.0\n",
       "3室3厅    33000.0\n",
       "4室3厅    33000.0\n",
       "5室2厅    33000.0\n",
       "5室3厅    38500.0\n",
       "5室1厅    42000.0\n",
       "5室4厅    45000.0\n",
       "6室1厅    50000.0\n",
       "7室3厅    50000.0\n",
       "6室4厅    52000.0\n",
       "4室4厅    56000.0\n",
       "8室6厅    60000.0\n",
       "6室3厅    60000.0\n",
       "7室4厅    62500.0\n",
       "6室2厅    64999.0\n",
       "8室3厅    65000.0\n",
       "8室4厅    70000.0\n",
       "9室3厅    74000.0\n",
       "7室2厅    77500.0\n",
       "3室4厅    95000.0\n",
       "7室6厅    95000.0\n",
       "Name: money, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layout_money.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2e48e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = (\n",
    "     Bar()\n",
    "     .add_xaxis(layout_money.index.to_list())\n",
    "     .add_yaxis(\"户型\",layout_money.tolist())\n",
    "     .reversal_axis()\n",
    "     .set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n",
    "     .set_global_opts(title_opts=opts.TitleOpts(title=\"不同户型的房租统计\"))\n",
    "     .render_notebook()\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "85262db1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"3fd48e12b8424b98aab39fc408c59d75\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_3fd48e12b8424b98aab39fc408c59d75 = echarts.init(\n",
       "                    document.getElementById('3fd48e12b8424b98aab39fc408c59d75'), 'white', {renderer: 'canvas'});\n",
       "                var option_3fd48e12b8424b98aab39fc408c59d75 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"bar\",\n",
       "            \"name\": \"\\u6237\\u578b\",\n",
       "            \"legendHoverLink\": true,\n",
       "            \"data\": [\n",
       "                4600.0,\n",
       "                5800.0,\n",
       "                13000.0,\n",
       "                6000.0,\n",
       "                11500.0,\n",
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       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
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       "        \"showContent\": true,\n",
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       "        \"textStyle\": {\n",
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       "                    \"curveness\": 0,\n",
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       "    \"yAxis\": [\n",
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       "            \"show\": true,\n",
       "            \"scale\": false,\n",
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       "            \"splitLine\": {\n",
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       "                    \"width\": 1,\n",
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       "                \"7\\u5ba46\\u5385\",\n",
       "                \"8\\u5ba43\\u5385\",\n",
       "                \"8\\u5ba44\\u5385\",\n",
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       "                \"9\\u5ba43\\u5385\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u4e0d\\u540c\\u6237\\u578b\\u7684\\u623f\\u79df\\u7edf\\u8ba1\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_3fd48e12b8424b98aab39fc408c59d75.setOption(option_3fd48e12b8424b98aab39fc408c59d75);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x21ec6e17f50>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb836932",
   "metadata": {},
   "source": [
    "### 分析面积对价钱的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "84efce7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"size\"] = df[\"size\"].str[:-1].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f52b3f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "size_money = df.groupby('size').money\n",
    "size_money_med = size_money.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "55ff22a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=(\n",
    "    Scatter()\n",
    "    .add_xaxis(size_money_med.index.to_list())\n",
    "    .add_yaxis(\"房租\", size_money_med.tolist())\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"Scatter-VisualMap(Color)\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(max_=150),\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "dfae2d8b",
   "metadata": {},
   "outputs": [
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   "execution_count": 28,
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       "密云        4\n",
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       "北京周边      1\n",
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     "execution_count": 28,
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   "execution_count": 30,
   "id": "ce7fb99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f = (\n",
    "    Pie()\n",
    "    .add(\"\", [list(z) for z in zip(zone_list.index, zone_list)])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title=\"行政区租房数量统计\"))\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\n",
    "    .set_colors([\"#EE6666\", \"#73C0DE\", \"#40B27D\", \"#FC8452\", \"#5470C6\", \"#91CC75\",\"#FAC858\",\"#FFC0CB\",])\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d08c5222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"47c22cfd67e547f590fdcc600a07d01c\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_47c22cfd67e547f590fdcc600a07d01c = echarts.init(\n",
       "                    document.getElementById('47c22cfd67e547f590fdcc600a07d01c'), 'white', {renderer: 'canvas'});\n",
       "                var option_47c22cfd67e547f590fdcc600a07d01c = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#EE6666\",\n",
       "        \"#73C0DE\",\n",
       "        \"#40B27D\",\n",
       "        \"#FC8452\",\n",
       "        \"#5470C6\",\n",
       "        \"#91CC75\",\n",
       "        \"#FAC858\",\n",
       "        \"#FFC0CB\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"pie\",\n",
       "            \"clockwise\": true,\n",
       "            \"data\": [\n",
       "                {\n",
       "                    \"name\": \"\\u671d\\u9633\",\n",
       "                    \"value\": 700\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6d77\\u6dc0\",\n",
       "                    \"value\": 296\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u987a\\u4e49\",\n",
       "                    \"value\": 255\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e30\\u53f0\",\n",
       "                    \"value\": 237\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u660c\\u5e73\",\n",
       "                    \"value\": 164\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u623f\\u5c71\",\n",
       "                    \"value\": 142\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u4e1c\\u57ce\",\n",
       "                    \"value\": 111\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u897f\\u57ce\",\n",
       "                    \"value\": 102\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5927\\u5174\",\n",
       "                    \"value\": 99\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u901a\\u5dde\",\n",
       "                    \"value\": 72\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u95e8\\u5934\\u6c9f\",\n",
       "                    \"value\": 35\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u77f3\\u666f\\u5c71\",\n",
       "                    \"value\": 23\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u71d5\\u90ca\",\n",
       "                    \"value\": 11\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5bc6\\u4e91\",\n",
       "                    \"value\": 4\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u6000\\u67d4\",\n",
       "                    \"value\": 1\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u5317\\u4eac\\u5468\\u8fb9\",\n",
       "                    \"value\": 1\n",
       "                }\n",
       "            ],\n",
       "            \"radius\": [\n",
       "                \"0%\",\n",
       "                \"75%\"\n",
       "            ],\n",
       "            \"center\": [\n",
       "                \"50%\",\n",
       "                \"50%\"\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8,\n",
       "                \"formatter\": \"{b}: {c}\"\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u671d\\u9633\",\n",
       "                \"\\u6d77\\u6dc0\",\n",
       "                \"\\u987a\\u4e49\",\n",
       "                \"\\u4e30\\u53f0\",\n",
       "                \"\\u660c\\u5e73\",\n",
       "                \"\\u623f\\u5c71\",\n",
       "                \"\\u4e1c\\u57ce\",\n",
       "                \"\\u897f\\u57ce\",\n",
       "                \"\\u5927\\u5174\",\n",
       "                \"\\u901a\\u5dde\",\n",
       "                \"\\u95e8\\u5934\\u6c9f\",\n",
       "                \"\\u77f3\\u666f\\u5c71\",\n",
       "                \"\\u71d5\\u90ca\",\n",
       "                \"\\u5bc6\\u4e91\",\n",
       "                \"\\u6000\\u67d4\",\n",
       "                \"\\u5317\\u4eac\\u5468\\u8fb9\"\n",
       "            ],\n",
       "            \"selected\": {},\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u884c\\u653f\\u533a\\u79df\\u623f\\u6570\\u91cf\\u7edf\\u8ba1\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_47c22cfd67e547f590fdcc600a07d01c.setOption(option_47c22cfd67e547f590fdcc600a07d01c);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x21ec907c390>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e4a26bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "subway_list = df.subway.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ec68527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     2253\n",
       "unique     223\n",
       "top          无\n",
       "freq       793\n",
       "Name: subway, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca6f6d4",
   "metadata": {},
   "source": [
    "可以得出，不在地铁站或车站附近的房源数据占总数据的1/3左右。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d5f4a30",
   "metadata": {},
   "source": [
    "# 分析朝向对价格的影响"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2257c69d",
   "metadata": {},
   "outputs": [],
   "source": [
    "location_money = df.groupby('location').money\n",
    "location_money_med = location_money.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "763ee024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[12000.0,\n",
       "  13000.0,\n",
       "  13000.0,\n",
       "  9500.0,\n",
       "  5250.0,\n",
       "  6800.0,\n",
       "  8900.0,\n",
       "  6800.0,\n",
       "  10750.0,\n",
       "  16500.0]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[location_money_med.tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f4f5c698",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = (\n",
    "    Radar()\n",
    "    .add_schema(\n",
    "        schema=[ \n",
    "            opts.RadarIndicatorItem(name=\"朝北\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝西北\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝西\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝西南\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝南\", max_=17000),\n",
    "             opts.RadarIndicatorItem(name=\"朝南北\", max_=17000),\n",
    "             opts.RadarIndicatorItem(name=\"朝东南\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝东\", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝东北 \", max_=17000),\n",
    "            opts.RadarIndicatorItem(name=\"朝东西\", max_=17000),\n",
    "        ]\n",
    "    )\n",
    "    .add(\"房租\",[location_money_med.tolist()])\n",
    "    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))\n",
    "    .set_global_opts(\n",
    "        legend_opts=opts.LegendOpts(selected_mode=\"single\"),\n",
    "        title_opts=opts.TitleOpts(title=\"朝向对房租的影响\"),\n",
    "    )\n",
    "    .render_notebook()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "22a6a842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"e3b0604f62e04c12a42a0174d68fb9d1\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_e3b0604f62e04c12a42a0174d68fb9d1 = echarts.init(\n",
       "                    document.getElementById('e3b0604f62e04c12a42a0174d68fb9d1'), 'white', {renderer: 'canvas'});\n",
       "                var option_e3b0604f62e04c12a42a0174d68fb9d1 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"radar\",\n",
       "            \"name\": \"\\u623f\\u79df\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    12000.0,\n",
       "                    13000.0,\n",
       "                    13000.0,\n",
       "                    9500.0,\n",
       "                    5250.0,\n",
       "                    6800.0,\n",
       "                    8900.0,\n",
       "                    6800.0,\n",
       "                    10750.0,\n",
       "                    16500.0\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": false,\n",
       "                \"position\": \"top\",\n",
       "                \"margin\": 8\n",
       "            },\n",
       "            \"itemStyle\": {\n",
       "                \"normal\": {}\n",
       "            },\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            },\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            },\n",
       "            \"rippleEffect\": {\n",
       "                \"show\": true,\n",
       "                \"brushType\": \"stroke\",\n",
       "                \"scale\": 2.5,\n",
       "                \"period\": 4\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u623f\\u79df\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u623f\\u79df\": true\n",
       "            },\n",
       "            \"selectedMode\": \"single\",\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"radar\": {\n",
       "        \"indicator\": [\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u5317\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u897f\\u5317\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u897f\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u897f\\u5357\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u5357\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u5357\\u5317\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u4e1c\\u5357\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u4e1c\",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u4e1c\\u5317 \",\n",
       "                \"max\": 17000\n",
       "            },\n",
       "            {\n",
       "                \"name\": \"\\u671d\\u4e1c\\u897f\",\n",
       "                \"max\": 17000\n",
       "            }\n",
       "        ],\n",
       "        \"name\": {\n",
       "            \"textStyle\": {}\n",
       "        },\n",
       "        \"splitLine\": {\n",
       "            \"show\": true,\n",
       "            \"lineStyle\": {\n",
       "                \"show\": true,\n",
       "                \"width\": 1,\n",
       "                \"opacity\": 1,\n",
       "                \"curveness\": 0,\n",
       "                \"type\": \"solid\"\n",
       "            }\n",
       "        },\n",
       "        \"splitArea\": {\n",
       "            \"show\": true,\n",
       "            \"areaStyle\": {\n",
       "                \"opacity\": 0\n",
       "            }\n",
       "        },\n",
       "        \"axisLine\": {\n",
       "            \"show\": true,\n",
       "            \"onZero\": true,\n",
       "            \"onZeroAxisIndex\": 0\n",
       "        }\n",
       "    },\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u671d\\u5411\\u5bf9\\u623f\\u79df\\u7684\\u5f71\\u54cd\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_e3b0604f62e04c12a42a0174d68fb9d1.setOption(option_e3b0604f62e04c12a42a0174d68fb9d1);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x21ec6d35650>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c15a603",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
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 },
 "nbformat": 4,
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